研究生: |
蔡志成 TSAI, Chih-Cheng |
---|---|
論文名稱: |
醫療影像辨識新興技術預測-以專利分析法探討 Medical Image Recognition Emerging Technology Prediction Patent Analysis Discussion |
指導教授: | 蘇友珊 |
學位類別: |
碩士 Master |
系所名稱: |
工業教育學系 Department of Industrial Education |
論文出版年: | 2020 |
畢業學年度: | 109 |
語文別: | 中文 |
論文頁數: | 127 |
中文關鍵詞: | 智慧醫療 、影像辨識技術 、費雪成長模型 、羅吉斯成長模型 、生命週期 |
英文關鍵詞: | smart healthcare, image recognition technology, Fisher-Pry Growth Model, Gowth Model, life cycle |
DOI URL: | http://doi.org/10.6345/NTNU202001525 |
論文種類: | 學術論文 |
相關次數: | 點閱:283 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
本研究以智慧醫療中的影像辨識技術為主題,以專利分析法和技術生命週期探討智慧醫療影像辨識技術相關的9項技術趨勢發展。應用國際專利分類號(IPC)、關鍵字和通過檢核之公告專利做檢索,以國際專利分類號探討智慧醫療中影像辨識技術所重視之分類為何種影像辨識技術。本研究以智慧醫療影像辨識技術相關的9項技術累積之專利數,作為衡量技術績效之專利指標,以費雪成長模型(Fisher-Pry Growth Model)和羅吉斯成長模型(Logistic Gowth Model),描述技術生命週期和衡量技術參透比率。
本研究使用以下專利技術與分類做檢索,且子技術又分為兩大類圖像數據分析(Image Data Analysis)包含3D立體(Three-Dimensional)、終端(Terminal)、像素(Pixel)、監控器(Monitor),而另一類影像數據採集(Image Data Collection)包含醫學影像 (Medical image)、解剖(Anatomical)、超音波(Ultrasound)、圖像數據(Image data)、外科手術(Surgical)等,研究結果表示醫療影像辨識目前處於成長階段,眼科光學影像(Ophthalmic Optical Imaging)及圖像顯示器(Image Display)……等,是近年來發展技術的重點,加入了包含AI與非AI以及純AI相關醫療影像辦識專利費雪成長模型比較,相較於非AI包含AI的新加入技術時間往後了大約10年發展時間,亦即未來還有很大的成長空間。
This research focuses on image recognition technology in smart medical technology, and explores 09 technological trends related to smart medical image recognition technology through patent analysis and technology life cycle. The International Patent Classification Number (IPC), keywords, and patents that have passed the inspection are used to search, and the international patent classification number is used to explore the classification of the image recognition technology that is valued in smart medical technology. In this study, the number of patents accumulated by 09 technologies related to smart medical image recognition technology is used as a patent indicator for measuring technical performance, and the Fisher-Pry Growth Model and the Logistic Gowth Model are used. Describe the technology life cycle and measure the technology penetration ratio.
This study uses the following patented technologies and classifications to search for Medical image, Anatomical, Pixel, Three-Dimensional, Terminal, Ultrasound, Monitor , Image data, Surgical etc. The results of the study found Image Data Analysis Image Data Collection, Ophthalmic Optical Imaging and Image Display etc. are the focus of technology development in recent years.
中文文獻
Chu, P. J., Yang, W. C., & Wu, C. T. (2019). FUTURE DE-VELOPMENT OF BIG DATA IN MEDICAL IMAGING.秀傳醫學雜誌,18(1),77-82.
Xiao, B. R.(2020)。在 U-Net 架構上運用遷移式學習於醫療影像之肝組織及腫瘤組織區塊辨識(未出版之碩士論文)。國立臺灣大學,台北市。
Xie, Y. L.(2019)。探討深度學習及影像特徵提取於心電圖和脈音圖時頻轉換的心臟疾病自動辨識(未出版之碩士論文)。國立成功大學,台南市。
大中國(2017)。牛科技:天網恢恢,國內天眼系統發展到什麼地步了?科技日報。取自https://gogonews.cc/article/2230553.html
工研院(2019)。工研院糖尿病視網膜病變診斷輔助分析技術。取自https://www.itri.org.tw/ListStyle.aspx?DisplayStyle=01_content&SiteID=1&MmmID=1036233376157517435&MGID=1036733443572024523
李侑珊(2019)。交大AI偵測血流 造福腎友。科技部。取自https://push.turnnewsapp.com/content/20191114001459-260114?fbclid=IwAR2fcNpz4w8W2Jfc3D71CY7nfQnr50RR9YqZlEI6yLrsE92FIg4zJN56Mgs
李春燕(2012)。基於專利訊息分析的技術生命週期判斷方法。現代情報,32,98-101。
張逸中(2018)。影像辨識的基礎是物理,是科學,絕不是工具!逸中軟體設計公司。取自http://blog.udn.com/yccsonar/112756160
張愛婷、陳賜賢(2018)。智慧醫療影像系統美國專利佈局分析。財團法人資訊工業策進會 產業情報研究所。CDOC20181114-003。
張櫻玲、李興中、范君凱& 彭學恭(2019)。系統性數值化醫學影像品質管理模式.臺灣醫事放射期刊,7(1),39-46.
郭宜欣、薛丞勛、高家祥& 孫英洲(2020)。淺談人工智慧在醫學影像的應用.臨床醫學月刊,85(1),14-20.
郭家宏(2019)。【工程師真偉大】日本正在研發辨識大便的 AI 馬桶,負責蒐集大便的數據分析師表示。科技報橘。取自https://buzzorange.com/techorange/2019/10/31/ai-recognition-excretion/?fbclid=IwAR3GU8IdRFXZLPaeoPwBIWuQHHCtg1Rsvaav1phxQbnnE-n-koaV51jNKZQ
陳昇瑋(2019)。人工智慧在台灣-產業轉型的契機與挑戰。台北市:天下雜誌。
陳昭成(2019)。雲端解剖學學習系統(未出版之碩士論文)。國立成功大學,台南市。
陳浩民(2019)。智慧診療之產業專利分析。財團法人專利檢所中心。取自https://www.psc.org.tw/upload/13/2019120915393531239.pdf
陳達仁、黃慕萱(2004)。專利醫訊與專利檢索。台北市:文華圖書。
陳達仁、黃慕萱(2018)。專利資訊檢索分析與策略。臺北市:華泰。
湯頌君(2019)。神經系統疾病昏厥之辨識與治療。台灣醫學,23(5),608-613。
黃膺任(2016)。影像處理技術原理與應用。嘉義大學。取自http://web.ncyu.edu.tw/~lanjc/lesson/C9/class/11.pdf
葉士緯、黃振榮(2017)。合作專利分類(CPC)實施現況之探討與應用。經濟部智慧財產局,217,5-14。
維基百科(2015)。國際專利分類。取自https://zh.wikipedia.org/wiki/%E5%9B%BD%E9%99%85%E4%B8%93%E5%88%A9%E5%88%86%E7%B1%BB
趙竑策、林耀鈴(2019)。應用機器學習方法於醫療影像辨識服務之自動建置系統。2019台灣網際網路研討會,PU107-11100-A06。
齊守良、嶽勇、辛軍&康雁(2013)。面向臨床腫瘤診療決策的多模態醫學影像融合。中國生物醫學工程學報,32(3),356-362。
劉家銓(2019)。使用全身骨掃描影像小型數據庫深度學習攝護腺癌骨轉移辨識系統。 中國醫藥大學。生物醫學影像暨放射科學學系碩士班學位論文。
蔡俊宇(2013)。醫學影像產業專利技術分析與佈局之探討(未出版之碩士論文)。國立台灣科技大學,台北市。
蔣沛芸(2020)。使用人工智慧於醫療影像的分析(未出版之碩士論文)。國立暨南大學,南投縣。
衛福部統計處(2019)。107年國人死因統計結果。取自https://www.mohw.gov.tw/cp-16-48057-1.html
賴奎魁、歐陽光、郭宗賢(2011)。整合專利家族與專利引用於新產品設計之研究。管理與系統,18(1),199-2 29。
謝怡萱(2017)。擴增實境之專利分析研究(未出版之碩士論文)。國立台北科技大學,台北市。
羅述謙、周果宏(2003)。醫學影像處理與分析。北京:科學出版社。
英文文獻
Aarnink, R., Pathak, S. D., De La Rosette, J. J., Debruyne, F. M., Kim, Y., & Wijkstra, H. (1998). Edge detection in prostatic ultrasound images using integrated edge maps.Ultrasonics, 36(1-5), 635-642.
Alan, P. J. R. (Ed.). (2018). Innovation Discovery: Network Anal-ysis Of Research And Invention Activity For Technology Management (Vol. 30). World Scientific.
Alsiddiky, A., Awwad, W., Bakarman, K., Fouad, H., & Mahmoud, N. M. (2020). Magnetic resonance imaging evaluation of vertebral tumor prediction using hierarchical hidden Markov random field model on internet of medical things (IOMT) platform.Measurement, 107772.
AlZu’bi, S., Shehab, M., Al-Ayyoub, M., Jararweh, Y., & Gupta, B. (2020). Parallel implementation for 3d medical volume fuzzy segmentation. Pattern Recognition Letters, 130, 312-318.
Aslni,A.,Mazzuca,Sobczuk,T.,Eivazi,S.,&Bekhrad,K(2018).Analysis of bioenergy technologies development based on life cycle and adaptation trends.Renewable Enery, 127, 1076-1086.
Bauer, S., Wiest, R., Nolte, L. P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor stud-ies. Physics in Medicine & Biology, 58(13), R97.
Borwonwatanadelok, P., Rattanapitak, W., & Udomhunsakul, S. (2009, February). Multi-focus image fusion based on sta-tionary wavelet transform and extended spatial frequency measurement. In 2009 International Conference on Electronic Computer Technology (pp. 77-81). IEEE.
Breitzman, A. F., & Mogee, M. E. (2002). The many applications of patent analysis. Journal of Information Science, 28(3), 187-205.
Brianzoni, E., Rossi, G., Ancidei, S., Berbellini, A., Capoccetti, F., Cidda, C., & Proietti, A. (2005). Radiotherapy planning: PET/CT scanner performances in the definition of gross tu-mour volume and clinical target volume. European journal of nuclear medicine and molecular imaging, 32(12), 1392-1399.
Chai, Y., Li, H., & Li, Z. (2011). Multifocus image fusion scheme using focused region detection and multiresolution. Optics Communications, 284(19), 4376-4389.
Chavan, S. S., & Talbar, S. N. (2015). Multimodality medical image fusion using M-band wavelet and Daubechies complex wavelet transform for radiation therapy. International Journal of Rough Sets and Data Analysis (IJRSDA), 2(2), 1-23.
Chen, C. M., Chen, C. M., Wu, H. C., & Tsai, C. S. (2013). Common carotid artery condition recognition technology using waveform features extracted from ultrasound spectrum images. Journal of Systems and Software, 86(1), 38-46.
Chen, T., Shi, Q., Zhu, M., He, T., Yang, Z., Liu, H., ... & Lee, C. (2019). Intuitive-augmented human-machine multidimensional nano-manipulation terminal using triboelectric stretchable strip sensors based on minimalist design. Nano Energy, 60, 440-448.
Chivukula, S., Everson, R., Linetsky, M., Heaney, A., Bonelli, L., Wang, M. B., & Bergsneider, M. (2016). Challenging Diagnosis and Surgical Management of a Symptomatic Sellar Spine. World neurosurgery, 91, 669-690.
Chlebus, G., Schenk, A., Moltz, J. H., van Ginneken, B., Hahn, H. K., & Meine, H. (2018). Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Scientific reports, 8(1), 1-7.
Chollet, F. (2017). Xception: Deep learning with depthwise sepa-rable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258).
Christ, P. F., Ettlinger, F., Grün, F., Elshaera, M. E. A., Lipkova, J., Schlecht, S., ... & Rempfler, M. (2017). Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv preprint arXiv:1702.05970.
Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981-1012.
Dimitrov, D. V. (2016). Medical internet of things and big data in healthcare. Healthcare informatics research, 22(3), 156-163.
Duncan, J. S., & Ayache, N. (2000). Medical image analysis: Progress over two decades and the challenges ahead. IEEE transactions on pattern analysis and machine intelli-gence, 22(1), 85-106.
Durand, D. J., Narayan, A. K., Rybicki, F. J., Burleson, J., Nagy, P., McGinty, G., & Duszak Jr, R. (2015). The health care value transparency movement and its implications for radi-ology. Journal of the American College of Radiology, 12(1), 51-58.
Ernst, H. (1997). The use of patent data for technological fore-casting: the diffusion of CNC-technology in the machine tool industry. Small business economics, 9(4), 361-381.
Ernst, H. (2003). Patent information for strategic technology management. World patent information, 25(3), 233-242.
Feng-Ping, A., & Zhi-Wen, L. (2019). Medical image segmentation algorithm based on feedback mechanism convolutional neural network. Biomedical Signal Processing and Control, 53, 101589.
Ferlay, J., Shin, H. R., Bray, F., Forman, D., Mathers, C., & Parkin, D. M. (2010). Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. International journal of can-cer, 127(12), 2893-2917.
Fourcade, A., & Khonsari, R. H. (2019). Deep learning in medical image analysis: A third eye for doctors. Journal of sto-matology, oral and maxillofacial surgery, 120(4), 279-288.
Fujii, H., Yoshida, K., & Sugimura, K. (2016). Research and development strategy in biological technologies: A patent data analysis of Japanese manufacturing firms. Sustainability, 8(4), 351.
Gallea, R., Ardizzone, E., Pirrone, R., & Gambino, O. (2013). Three-dimensional fuzzy kernel regression framework for registration of medical volume data. Pattern recogni-tion, 46(11), 3000-3016.
George, J. J., Renjith, V. R., George, P., & George, A. S. (2019). Application of fuzzy failure mode effect and criticality anal-ysis on unloading facility of LNG terminal. Journal of Loss Prevention in the Process Industries, 61, 104-113.
Haase, A., Landwehr, G., & Umbach, E. (1997). R ntgen Centen-nial: X-rays in Natural and Life Sciences. World Scientific.
Hadjerci, O., Hafiane, A., Morette, N., Novales, C., Vieyres, P., & Delbos, A. (2016). Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications, 61, 64-77.
Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. Review of Economics and statistics, 81(3), 511-515.
He, D., Liu, L., Miao, S., Tong, X., & Sheng, M. (2019). Proba-bilistic guided polycystic ovary syndrome recognition using learned quality kernel. Journal of Visual Communication and Image Representation, 63, 102587.
Heimann, T., Meinzer, H. P., & Wolf, I. (2007). A statistical de-formable model for the segmentation of liver CT volumes. 3D Segmentation in the clinic: A grand challenge, 161-166.
Hentschel, B., Oehler, W., Strauß, D., Ulrich, A., & Malich, A. (2011). Definition of the CTV prostate in CT and MRI by us-ing CT–MRI image fusion in IMRT planning for prostate cancer. Strahlentherapie und onkologie, 187(3), 183-190.
Huang, C. J., Trappey, A. J., & Wu, C. Y. (2008). Develop a formal ontology engineering methodology for technical knowledge definition in r&d knowledge management. In Collaborative Product and Service Life Cycle Management for a Sustainable World (pp. 495-502). Springer, London.
Huang, W., & Jing, Z. (2007). Evaluation of focus measures in multi-focus image fusion. Pattern recognition letters, 28(4), 493-500.
James, A. P., & Dasarathy, B. V. (2014). Medical image fusion: A survey of the state of the art. Information fusion, 19, 4-19.
Jiao, S., Li, G., Zhang, D., Xu, Y., Liu, J., & Li, G. (2020). Anatomic versus non-anatomic resection for hepatocellular carcinoma, do we have an answer? A meta-analysis. International Journal of Surgery.
Ke, Q., Zhang, J., Wei, W., Połap, D., Woźniak, M., Kośmider, L., & Damaševĭcius, R. (2019). A neuro-heuristic approach for recognition of lung diseases from X-ray images. Expert Systems with Applications, 126, 218-232.
Kelsey, A. H., McCulloch, V., Gillingwater, T. H., Findlater, G. S., & Paxton, J. Z. (2020). Anatomical Sciences at the University of Edinburgh: Initial experiences of teaching anatomy online. Translational Research in Anatomy, 100065.
Koh, D. M., & Collins, D. J. (2007). Diffusion-weighted MRI in the body: applications and challenges in oncology. American Journal of Roentgenology, 188(6), 1622-1635.
Kondoh, H., Nishitani, H., & Washiashi, T. (2001, June). Users' behavior and performance of general-purpose color CRT monitors of PACS in the wards. In International Congress Series (Vol. 1230, pp. 785-790). Elsevier.
Kong, Y., Jiang, Y., & Lou, J. (2019). Terminal computing for Sylvester equations solving with application to intelligent control of redundant manipulators. Neurocomputing, 335, 119-130.
Koroutchev, K., & Korutcheva, E. (2009). Detecting the most un-usual part of two-and three-dimensional digital imag-es. Pattern recognition, 42(8), 1684-1692.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
Lee, C., Jeon, J., & Park, Y. (2011). Monitoring trends of techno-logical changes based on the dynamic patent lattice: A modi-fied formal concept analysis approach. Technological Fore-casting and Social Change, 78(4), 690-702.
Lehmann, T. M., Güld, M. O., Deselaers, T., Keysers, D., Schubert, H., Spitzer, K., ... & Wein, B. B. (2005). Automatic categorization of medical images for content-based retrieval and data mining. Computerized Medical Imaging and Graphics, 29(2-3), 143-155.
Li, B., Tian, L., Kang, Y., & Yu, X. (2008, May). Parallel multimodal medical image fusion in 3D conformal radiotherapy treatment planning. In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering (pp. 2600-2604). IEEE.
Li, H., Zhang, B., Zhang, Y., Liu, W., Mao, Y., Huang, J., & Wei, L. (2020). A semi-automated annotation algorithm based on weakly supervised learning for medical imag-es. Biocybernetics and Biomedical Engineering.
Li, J., Udupa, J. K., Tong, Y., Wang, L., & Torigian, D. A. (2020). LinSEM: Linearizing segmentation evaluation metrics for medical images. Medical Image Analysis, 60, 101601.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
Liu, D., Zhang, L., Luo, T., Tao, L., & Wu, Y. (2020). Towards Interpretable and Robust Hand Detection via Pixel-wise Pre-diction. arXiv preprint arXiv:2001.04163.
Lotz, J. M., Hoffmann, F., Lotz, J., Heldmann, S., Trede, D., Oetjen, J., ... & Guntinas-Lichius, O. (2017). Integration of 3D multimodal imaging data of a head and neck cancer and ad-vanced feature recognition. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 1865(7), 946-956.
Lu, G., & Fei, B. (2014). Medical hyperspectral imaging: a re-view. Journal of biomedical optics, 19(1), 010901.
Luo, J., Wu, M., Gopukumar, D., & Zhao, Y. (2016). Big data ap-plication in biomedical research and health care: a literature review. Biomedical informatics insights, 8, BII-S31559.
Luo, K., Lu, J., Zhu, K. Q., Gao, W., Wei, J., & Zhang, M. (2019). Layout-aware information extraction from semi-structured medical images. Computers in biology and medicine, 107, 235-247.
Machucho-Cadena, R., Rivera-Rovelo, J., & Bayro-Corrochano, E. (2014). Geometric techniques for 3D tracking of ultrasound sensor, tumor segmentation in ultrasound images, and 3D reconstruction. Pattern recognition, 47(5), 1968-1987.
Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle in-troduction to deep learning in medical image pro-cessing. Zeitschrift für Medizinische Physik, 29(2), 86-101.
Mansor, M. N., Hariharan, M., Basah, S. N., & Yaacob, S. (2013). New newborn jaundice monitoring scheme based on combination of pre-processing and color detection meth-od. Neurocomputing, 120, 258-261.
Mazura, J. C., Juluru, K., Chen, J. J., Morgan, T. A., John, M., & Siegel, E. L. (2012). Facial recognition software success rates for the identification of 3D surface reconstructed facial images: implications for patient privacy and security. Journal of digital imaging, 25(3), 347-351.
Meyer, P. S., Yung, J. W., & Ausubel, J. H. (1999). A primer on logistic growth and substitution: the mathematics of the Loglet Lab software. Technological forecasting and social change, 61(3), 247-271.
Miller, E., Li, Z., Mentis, H., Park, A., Zhu, T., & Banerjee, N. (2020). RadSense: Enabling one hand and no hands interac-tion for sterile manipulation of medical images using Doppler radar. Smart Health, 15, 100089.
Moris, D., Tsilimigras, D. I., Kostakis, I. D., Ntanasis-Stathopoulos, I., Shah, K. N., Felekouras, E., & Pawlik, T. M. (2018). Anatomic versus non-anatomic resection for hepatocellular carcinoma: a systematic review and meta-analysis. European Journal of Surgical Oncology, 44(7), 927-938.
Oakden-Rayner, L. (2020). Exploring Large-scale Public Medical Image Datasets. Academic radiology, 27(1), 106-112.
Parvathy, V. S., Pothiraj, S., & Sampson, J. (2020). Optimal Deep Neural Network model based multimodality fused medical image classification. Physical Communication, 101119.
Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla Jr, C. N., & Costa, Y. M. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 105532.
Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018). Artificial intelligence as a medical device in radiology: ethi-cal and regulatory issues in Europe and the United States. Insights into imaging, 9(5), 745-753.
Popper, E. T., & Buskirk, B. D. (1992). Technology life cycles in industrial markets. Industrial Marketing Management, 21(1), 23-31.
Rapoport, R. J., Parker, L., Levin, D. C., & Hiatt, M. D. (2016). A large state Medicaid outpatient advanced imaging utilization management program: substantial savings without the need for denials. Medical Care Research and Review, 73(3), 369-380.
Regge, D., Mazzetti, S., Giannini, V., Bracco, C., & Stasi, M. (2017). Big data in oncologic imaging. La radiologia medi-ca, 122(6), 458-463.
Rizzo, G., Cattaneo, G. M., Castellone, P., Castiglioni, I., Ceresoli, G. L., Messa, C., ... & Fazio, F. (2004). Multi-modal medical image integration to optimize radiotherapy planning in lung cancer treatment. Annals of biomedical engineering, 32(10), 1399-1408.
Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
Rusko, L., Bekes, G., Nemeth, G., & Fidrich, M. (2007). Fully automatic liver segmentation for contrast-enhanced CT im-ages. MICCAI Wshp. 3D Segmentation in the Clinic: A Grand Challenge, 2(7).
Sarwar, A., Boland, G., Monks, A., & Kruskal, J. B. (2015). Metrics for radiologists in the era of value-based health care de-livery. Radiographics, 35(3), 866-876.
Shaw, R. A., Mansfield, J. R., Rempel, S. P., Low-Ying, S., Kupriyanov, V. V., & Mantsch, H. H. (2000). Analysis of bi-omedical spectra and images: from data to diagnosis. Journal of Molecular Structure: THEOCHEM, 500(1-3), 129-138.
Sheikh, N., Gomez, F. A., Cho, Y., & Siddappa, J. (2011, July). Forecasting of advanced electronic packaging technologies using bibliometric analysis and Fisher-Pry diffusion model. In 2011 Proceedings of PICMET'11: Technology Management in the Energy Smart World (PICMET) (pp. 1-20). IEEE.
Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221-248.
Shusharina, N., Söderberg, J., Edmunds, D., Löfman, F., Shih, H., & Bortfeld, T. (2020). Automated delineation of the clinical target volume using anatomically constrained 3D expansion of the gross tumor volume. Radiotherapy and Oncology, 146, 37-43.
Smith-Bindman, R., Miglioretti, D. L., Johnson, E., Lee, C., Fei-gelson, H. S., Flynn, M., ... & Solberg, L. I. (2012). Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996-2010. Jama, 307(22), 2400-2409.
Sotiras, A., Davatzikos, C., & Paragios, N. (2013). Deformable medical image registration: A survey. IEEE transactions on medical imaging, 32(7), 1153-1190.
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning?. IEEE transactions on medical imaging, 35(5), 1299-1312.
Tóth, M., Ruskó, L., & Csébfalvi, B. (2016). Automatic recognition of anatomical regions in three-dimensional medical im-ages. Computers in biology and medicine, 76, 120-133.
Waller, L. P., Deshpande, V., & Pyrsopoulos, N. (2015). Hepato-cellular carcinoma: A comprehensive review. World journal of hepatology, 7(26), 2648.
Xiao, Y., Zhou, L., & Chen, W. (2020). Secured single-pixel ghost holography. Optics and Lasers in Engineering, 128, 106045.
Yang, Q. Z., Sun, J. P., & Zhu, C. F. (2006). Medical device in-novation methods and case studies (Vol. 7, pp. 232-238). SIMTech technical reports.
Ye, Z., Wang, H., Xiong, J., & Wang, K. (2020). Simultaneous full-color single-pixel imaging and visible watermarking using Hadamard-Bayer illumination patterns. Optics and Lasers in Engineering, 127, 105955.
Yoon, B., Phaal, R., & Probert, D. (2008). Structuring technological information for technology roadmapping: data mining approach. AIKED, 8, 417-422.
Zhang, X., Zeng, Q., & Sheu, J. B. (2019). Modeling the produc-tivity and stability of a terminal operation system with quay crane double cycling. Transportation Research Part E: Lo-gistics and Transportation Review, 122, 181-197.
Zhao, K., Wang, C., Hu, J., Yang, X., Wang, H., Li, F., ... & Wang, X. (2015). Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model. Science China Life Sciences, 58(7), 666-673.